Put the ipynb file and html file in the github branch you created in the last assignment and submit the link to the commit in brightspace
from plotly.offline import init_notebook_mode
import plotly.io as pio
import plotly.express as px
init_notebook_mode(connected=True)
pio.renderers.default = "plotly_mimetype+notebook"
#load data
df = px.data.gapminder()
df.head()
| country | continent | year | lifeExp | pop | gdpPercap | iso_alpha | iso_num | |
|---|---|---|---|---|---|---|---|---|
| 0 | Afghanistan | Asia | 1952 | 28.801 | 8425333 | 779.445314 | AFG | 4 |
| 1 | Afghanistan | Asia | 1957 | 30.332 | 9240934 | 820.853030 | AFG | 4 |
| 2 | Afghanistan | Asia | 1962 | 31.997 | 10267083 | 853.100710 | AFG | 4 |
| 3 | Afghanistan | Asia | 1967 | 34.020 | 11537966 | 836.197138 | AFG | 4 |
| 4 | Afghanistan | Asia | 1972 | 36.088 | 13079460 | 739.981106 | AFG | 4 |
Recreate the barplot below that shows the population of different continents for the year 2007.
Hints:
# YOUR CODE HERE
# Filter data for the year 2007
df_2007 = df[df['year'] == 2007]
# Sort the data by continent and population
df_2007_sorted = df_2007.sort_values(by=['continent', 'pop']).groupby('continent').sum().reset_index()
# Create the barplot
fig = px.bar(
df_2007_sorted,
x = 'pop',
y = 'continent',
color= 'continent', # Different colors for each continent
title= 'Population of Continents in 2007'
)
# Customize layout of the figure
fig.update_layout(
showlegend=False, # Hide legend
)
# Show the plot
fig.show()
# YOUR CODE HERE
# Filter data for the year 2007
df_2007 = df[df['year'] == 2007]
# Sort the data by continent and population
df_2007_sorted = df_2007.sort_values(by=['continent', 'pop']).groupby('continent').sum().reset_index()
# Create the barplot
fig = px.bar(
df_2007_sorted,
x = 'pop',
y = 'continent',
color= 'continent', # Different colors for each continent
title= 'Population of Continents in 2007'
)
# Customize layout of the figure
fig.update_layout(
showlegend=False, # Hide legend,
yaxis={'categoryorder': 'total ascending'} # Sort the order of the continent for the visualisation
)
# Show the plot
fig.show()
Add text to each bar that represents the population
# YOUR CODE HERE
# Filter data for the year 2007
df_2007 = df[df['year'] == 2007]
# Sort the data by continent and population
df_2007_sorted = df_2007.sort_values(by=['continent', 'pop']).groupby('continent').sum().reset_index()
# Create the barplot
fig = px.bar(
df_2007_sorted,
x = 'pop',
y = 'continent',
color= 'continent', # Different colors for each continent
text_auto='.2s',
title= 'Population of Continents in 2007'
)
# Customize layout of the figure
fig.update_layout(
showlegend=False, # Hide legend,
yaxis={'categoryorder': 'total ascending'}, # Sort the order of the continent for the visualisation
)
# Show the plot
fig.show()
Thus far we looked at data from one year (2007). Lets create an animation to see the population growth of the continents through the years
# YOUR CODE HERE
# Sort the data by continent and population
df_sorted = df.sort_values(by=['continent', 'pop'])
# Create the barplot
animated_fig = px.histogram(
df_sorted,
x = 'pop',
y = 'continent',
color= 'continent', # Different colors for each continent
title= 'Population of Continents over time'
)
# Create animation
animated_fig = px.histogram(df, x="pop", y="continent", color="continent",
animation_frame="year", range_x=[0,4000000000])
# Customize layout of the figure
animated_fig.update_layout(
showlegend=False, # Hide legend,
yaxis={'categoryorder': 'total ascending'}, # Sort the order of the continent for the visualisation
)
# Show figure
animated_fig.show()
Instead of the continents, lets look at individual countries. Create an animation that shows the population growth of the countries through the years
# YOUR CODE HERE
# Create animation
animated_fig = px.histogram(df, x="pop", y="country", color="country",
animation_frame="year", range_x=[0,1400000000])
# Customize layout of the figure
animated_fig.update_layout(
showlegend=False, # Hide legend,
yaxis={'categoryorder': 'total ascending'}, # Sort the order of the continent for the visualisation
title= 'Population of Countries over time'
)
# Show figure
animated_fig.show()
Clean up the country animation. Set the height size of the figure to 1000 to have a better view of the animation
# YOUR CODE HERE
# Create animation
animated_fig = px.histogram(df, x="pop", y="country", color="country",
animation_frame="year", range_x=[0,1400000000])
# Customize layout of the figure
animated_fig.update_layout(
showlegend=False, # Hide legend,
yaxis={'categoryorder': 'total ascending'}, # Sort the order of the continent for the visualisation
height=1000,
title= 'Population of Countries over time'
)
# Show figure
animated_fig.show()
# YOUR CODE HERE
# # Sort the data by continent and population
# df_sorted = df.sort_values(by=['country', 'pop'])
# # Create the barplot
# animated_fig = px.histogram(
# df_sorted,
# x = 'pop',
# y = 'country',
# color= 'country', # Different colors for each continent
# title= 'Population of Top 10 Countries over time'
# )
# Create animation
animated_fig = px.histogram(df,
x="pop",
y="country",
color="country",
animation_frame="year",
range_x=[0,1400000000],
range_y=[131.5,142]
)
# Customize layout of the figure
animated_fig.update_layout(
showlegend=False,
yaxis={'categoryorder': 'total ascending'},
height=1000,
title='Population of Top 10 Countries over time'
)
# Show figure
animated_fig.show()